A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases.
BACKGROUND
The aim of this study was to combine the new automated Access [-2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural network (ANN) or logistic regression (LR) model to enhance discrimination between patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and to detect aggressive PCa.


METHODS
Sera from 311 PCa patients and 275 NEM patients were measured with the p2PSA, total PSA (tPSA) and free PSA (fPSA) assays on Access immunoassay technology (Beckman Coulter, Fullerton, CA) within the 0-30 ng/ml tPSA range. Four hundred seventy-five patients (264 PCa, 211 NEM) had a tPSA of 2-10 ng/ml. LR models and leave-one-out (LOO) ANN models with Bayesian regularization by using tPSA, %fPSA, p2PSA/fPSA (%p2PSA), age and prostate volume were constructed and compared by receiver-operating characteristic (ROC) curve analysis.


RESULTS
The ANN and LR model each utilizing %p2PSA, %fPSA, tPSA and age, but without prostate volume, reached the highest AUCs (0.85 and 0.84) and best specificities (ANN: 62.1% and 45.5%; LR: 53.1% and 41.2%) compared with tPSA (22.7% and 11.4%) and %fPSA (45.5% and 26.1%) at 90% and 95% sensitivity. The %p2PSA furthermore distinguished better than tPSA and %fPSA between pT2 and pT3, and Gleason sum <7 and >or=7 PCa.


CONCLUSIONS
The automated p2PSA assay offers a new tool to improve PCa detection, and especially aggressive PCa detection. Incorporation of %p2PSA into an ANN and LR model further enhances the diagnostic accuracy to differentiate between malignant and non-malignant prostate diseases.
Stephan
Carsten
C
Department of Urology, Charité-Universitätsmedizin Berlin, Berlin, Germany. carsten.stephan@charite.de
Kahrs
Anna-Maria
AM
Cammann
Henning
H
Lein
Michael
M
Schrader
Mark
M
Deger
Serdar
S
Miller
Kurt
K
Jung
Klaus
K
D000368:Aged; D001331:Automation / Q000379:methods; D001604:Berlin; D006801:Humans; D007118:Immunoassay / Q000379:methods; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011467:Prostate / Q000033:anatomy & histology / Q000473:pathology; D017430:Prostate-Specific Antigen / Q000032:analysis*; D011469:Prostatic Diseases / Q000175:diagnosis / Q000473:pathology*; D011470:Prostatic Hyperplasia / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000473:pathology*; D012044:Regression Analysis; D018401:Sample Size; D012680:Sensitivity and Specificity
D017430:Prostate-Specific Antigen